Classification and Regression Trees, Cart TM - A user manual for identifying indicators of vulnerability to famine and chronic food insecurity + disk (IFPRI, 1999, 56 p.): Appendix 2: A Condensed Example of Regression-Tree Output (full output on diskette)

Classification and Regression Trees, Cart TM - A user manual for identifying indicators of vulnerability to famine and chronic food insecurity + disk (IFPRI, 1999, 56 p.)

		(introduction...)
		Preface
		Acknowledgments
		1. Introduction
		2. Overview of CART
		3. Basic Principles of Cart Methodology
		4. Regression Trees: An Overview
		5. CART Software and Program Codes
		6. Refining CART Analyses
		7. Conclusions
		Appendix 1: Condensed Examples of Classification-Tree Output (full output on diskette)
		Appendix 2: A Condensed Example of Regression-Tree Output (full output on diskette)
		References
		Back Cover

Appendix 2: A Condensed Example of Regression-Tree Output (full output on diskette)

	Node 1 was split on variable NDVIMNMX A case goes left if variable NDVIMNMX £ 0.335000 Improvement = 95.212097 C.T. = 0.439885E+0.05
Figure	Node	Cases			Average		Standard Deviation
	1	462			10.902165		19.447115
	2	174			23.455744		25.835199
	-3	288			3.317708		7.119483
	Surrogate			Split		Association		Improvement
	1 KRMTMNMN		s	0.335000		0.931034		88.178185
	2 NDVIMXMX		s	0.475000		0.827586		84.152443
	3 BEGAMXMN		s	0.365000		0.793103		62.045887
	4 BEGAMNMN		s	0.300000		0.793103		68.704895
	5 KRMTMXMN		s	0.475000		0.793103		79.163147
	Competitor			Split				Improvement
	1 KRMTMNMN			0.335000				88.177315
	2 NDVIMXMX			0.475000				84.151741
	3 KRMTMX			0.435000				81.044876
	4 KRMTMXMN			0.475000				79.162216
	5 KRMTMN			0.285000				78.868150
	Node 2 was split on variable MZSHTTRD A case goes left if variable MZSHTTRD £ 31.389999 Improvement = 58.391998 C.T = 0.269771E+0.05
Figure	Node	Cases			Average		Standard Deviation
	2	174			23.455744		25.835199
	-1	65			39.580002		28.492435
	-2	109			13.840366		18.272223
	Surrogate			Split		Associations		Improvement
	1 MZSHTTDV		s	-0.780000		0.861539		38.192043
	2 CERLPPDV		s	-0.085000		0.492308		26.714172
	3 BELGMNDV		s	-0.855000		0.492308		39.261301
	4 BEGAMNDV		s	-0.620000		0.369231		18.076315
	5 KRMTMNDV		s	-1.150000		0.323077		27.505999
	Competitor			Split				Improvement
	1 BELGMNDV			-0.905000				43.058678
	2 BEGAMNDV			-0.055000				40.379753
	3 MZSHTTDV			-1.185000				39.435616
	4 KRMTMXDV			-1.905000				37.680218
	5 KRMTMNDV			-0.900000				32.776260

Note: C.T. stands for Complexity Threshold (the complexity parameter used in tree pruning).